Computer data distribution architecture for efficient distribution and synchronization of plotting processing and data

ABSTRACT

Described are methods, systems and computer readable media for computer data distribution architecture for efficient distribution and synchronization of plotting processing and data.

This application claims the benefit of U.S. Provisional Application No.62/549,908, entitled “COMPUTER DATA SYSTEM” and filed on Aug. 24, 2017,which is incorporated herein by reference in its entirety.

Embodiments relate generally to computer data systems, and moreparticularly, to methods, systems and computer readable media forcomputer data distribution architecture for efficient plotting datasynchronization using remote query processors.

Some conventional computer data systems may maintain data in one or moredata sources that may include data objects such as tables. Theseconventional systems may include clients that access tables from eachdata source to create visualizations of the data. In such data systems,a need may exist to provide systems and methods for efficientsynchronization of dynamically changing plotting data, in order toreduce memory usage of an individual client and to enable redundancy,high-availability, scalability, and allow parallelization of plottingprocessing across multiple clients. In such data systems, a need mayalso exist to enable local modification of plots without having tocontact a server in order to provide more responsive user interactionsand to minimize communications with the server.

Embodiments were conceived in light of the above mentioned needs,problems and/or limitations, among other things.

Some implementations (first implementations) include a computer databasesystem, one or more processors, and computer readable storage coupled tothe one or more processors. The computer readable storage can havestored thereon instructions that, when executed by the one or moreprocessors, cause the one or more processors to perform operations. Theoperations can include receiving, at a remote query processor, a plotcommand to generate a plot at a client computer, the plot commandreferencing a first object, the first object being updatable bypropagating updates through an update propagation graph associated withthe first object. The operations can include generating, at the remotequery processor, a plotting data structure comprising an export objecthandle referencing at least a portion of the first object. Theoperations can include transmitting, at the remote query processor, oneor more messages to the client computer, the one or more messagescomprising the plotting data structure and an initial snapshot of thefirst object. The operations can include automatically subscribing, atthe remote query processor, the client computer to receive consistentupdates to the first object. The operations can include receiving, atthe client computer, the one or more messages comprising the plottingdata structure and the initial snapshot from the remote query processor.The operations can include creating, at the client computer, a secondobject to represent a replica of the portion of the first objectreferenced by the export object handle. The operations can includestoring, at the client computer, the initial snapshot as the replica ofthe portion of the first object referenced by the export object handle.The operations can include assigning, at the client computer, thereplica as an input to a figure to be displayed in a graphical userinterface. The operations can include generating, at the clientcomputer, a graphical figure comprising the plot based on the plottingdata structure and the replica of the portion of the first objectreferenced by the export object handle. The operations can includeadding at the remote query processor a first listener to the updatepropagation graph as a dependent of the first object. The operations caninclude receiving, at the first listener, an update notificationindicating an update to the first object. The operations can includesending, by the remote query processor, a notification to the clientcomputer including an indication of the change to the first object and acopy of any changed data. The operations can include, responsive toreceiving the notification at the client computer, updating the replicaof the portion of the first object referenced by the export objecthandle. The operations can include updating, at the client computer, thegraphical figure comprising the plot based on the plotting datastructure and the updated replica of the portion of the first objectreferenced by the export object handle.

In some first implementations, the plotting data structure comprises theinitial snapshot. In some first implementations, the operations canfurther include: receiving, at the client computer, a request for thegraphical figure from a remote computer; and transmitting, at the clientcomputer, the graphical figure in an image format to the remotecomputer. In some first implementations, the image format is selectedfrom a group consisting of JPEG, GIF, PNG, SVG, and PDF. In some firstimplementations, the updating the graphical figure is performed after atleast a portion of the graphical figure is visible in the graphical userinterface. In some first implementations, the updating the graphicalfigure is throttled such that the updating is performed as part of abatch update. In some first implementations, the plotting data structurecomprises a second export object handle referencing a second object todefine an attribute of the plot. In some first implementations, thefirst object is a table and the export object handle is an export tablehandle. In some first implementations, the operations further includedetermining that the graphical figure is not being displayed by theclient computer, and, responsive to the determining that the graphicalfigure is not being displayed, setting a mode of the plot to a sleepmode. In some first implementations, the sleep mode ignores or preventsredraw events for the plot.

Some implementations (second implementations) include a method that caninclude receiving, at a remote query processor, a plot command togenerate a plot at a client computer, the plot command referencing afirst object, the first object being updatable by propagating updatesthrough an update propagation graph associated with the first object.The method can include generating, at the remote query processor, aplotting data structure comprising an export object handle referencingat least a portion of the first object. The method can includetransmitting, at the remote query processor, one or more messages to theclient computer, the one or more messages comprising the plotting datastructure and an initial snapshot of the first object. The method caninclude receiving, at the client computer, the one or more messagescomprising the plotting data structure and the initial snapshot from theremote query processor. The method can include creating, at the clientcomputer, a second object to represent a replica of the portion of thefirst object referenced by the export object handle. The method caninclude storing, at the client computer, the initial snapshot as thereplica of the portion of the first object referenced by the exportobject handle. The method can include assigning, at the client computer,the replica as an input to a figure to be displayed in a graphical userinterface. The method can include generating, at the client computer, agraphical figure comprising the plot based on the plotting datastructure and the replica of the portion of the first object referencedby the export object handle. The method can include adding at the remotequery processor a first listener to the update propagation graph as adependent of the first object. The method can include receiving, at thefirst listener, an update notification indicating an update to the firstobject. The method can include sending, by the remote query processor, anotification to the client computer including an indication of thechange to the first object and a copy of any changed data. The methodcan include, responsive to receiving the notification at the clientcomputer, updating the replica of the portion of the first objectreferenced by the export object handle. The method can include updating,at the client computer, the graphical figure comprising the plot basedon the plotting data structure and the updated replica of the portion ofthe first object referenced by the export object handle.

In some second implementations, the plotting data structure comprisesthe initial snapshot. In some second implementations, the method canfurther include: receiving, at the client computer, a request for thegraphical figure from a remote computer; and transmitting, at the clientcomputer, the graphical figure in an image format to the remotecomputer. In some second implementations, the image format is selectedfrom a group consisting of JPEG, GIF, PNG, SVG, and PDF. In some secondimplementations, the updating the graphical figure is performed after atleast a portion of the graphical figure is visible in the graphical userinterface. In some second implementations, the updating the graphicalfigure is throttled such that the updating is performed as part of abatch update. In some second implementations, the plotting datastructure comprises a second export object handle referencing a secondobject to define an attribute of the plot. In some secondimplementations, the method further comprising automaticallysubscribing, at the remote query processor, the client computer toreceive consistent updates to the first object. In some secondimplementations, the first object is a table and the export objecthandle is an export table handle. In some second implementations, themethod further includes determining that the graphical figure is notbeing displayed by the client computer, and responsive to thedetermining that the graphical figure is not being displayed, setting amode of the plot to a sleep mode. In some second implementations, thesleep mode stops updates to the first object from being received.

Some implementations (third implementations) include a nontransitorycomputer readable medium having stored thereon software instructionsthat, when executed by one or more processors, cause the one or moreprocessors to perform operations. The operations can include receiving,at a remote query processor, a plot command to generate a plot at aclient computer, the plot command referencing a first object, the firstobject being updatable by propagating updates through an updatepropagation graph associated with the first object. The operations caninclude generating, at the remote query processor, a plotting datastructure comprising an export object handle referencing at least aportion of the first object. The operations can include transmitting, atthe remote query processor, one or more messages to the client computer,the one or more messages comprising the plotting data structure and aninitial snapshot of the first object. The operations can includereceiving, at the client computer, the one or more messages comprisingthe plotting data structure and the initial snapshot from the remotequery processor. The operations can include creating, at the clientcomputer, a second object to represent a replica of the portion of thefirst object referenced by the export object handle. The operations caninclude storing, at the client computer, the initial snapshot as thereplica of the portion of the first object referenced by the exportobject handle. The operations can include assigning, at the clientcomputer, the replica as an input to a figure to be displayed in agraphical user interface. The operations can include generating, at theclient computer, a graphical figure comprising the plot based on theplotting data structure and the replica of the portion of the firstobject referenced by the export object handle. The operations caninclude adding at the remote query processor a first listener to theupdate propagation graph as a dependent of the first object. Theoperations can include receiving, at the first listener, an updatenotification indicating an update to the first object. The operationscan include sending, by the remote query processor, a notification tothe client computer including an indication of the change to the firstobject and a copy of any changed data. The operations can include,responsive to receiving the notification at the client computer,updating the replica of the portion of the first object referenced bythe export object handle. The operations can include updating, at theclient computer, the graphical figure comprising the plot based on theplotting data structure and the updated replica of the portion of thefirst object referenced by the export object handle.

In some third implementations, the plotting data structure comprises theinitial snapshot. In some third implementations, the operations alsoinclude receiving, at the client computer, a request for the graphicalfigure from a remote computer, and, transmitting, at the clientcomputer, the graphical figure in an image format to the remote computerin response to the request from the remote computer. In some thirdimplementations, the updating the graphical figure is performed onlywhen at least a portion of the graphical figure is visible in thegraphical user interface. In some third implementations, the updatingthe graphical figure is throttled such that the updating is performed aspart of a batch update. In some third implementations, the plotting datastructure comprises a second export object handle referencing a secondobject to define an attribute of the plot. In some thirdimplementations, the operations also include automatically subscribing,at the remote query processor, the client computer to receive consistentupdates to the first object. In some third implementations, the firstobject is a table and the export object handle is an export tablehandle. In some third implementations, the operations also includedetermining that the graphical figure is not being displayed by theclient computer, and, responsive to the determining that the graphicalfigure is not being displayed, setting a mode of the plot to a sleepmode.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an example computer data system showing anexample data distribution configuration, in accordance with someimplementations.

FIG. 2 is a diagram of an example computer data system showing anexample administration/process control arrangement, in accordance withsome implementations.

FIG. 3 is a diagram of an example computing device configured forefficient distribution and synchronization of plotting processing anddata, in accordance with at least one implementation.

FIG. 4 is a flowchart of an example method of efficient distribution andsynchronization of plotting processing and data in accordance with someimplementations.

FIG. 5 is a flowchart of an example method of efficient distribution andsynchronization of plotting processing and data in accordance with someimplementations.

FIGS. 6A-F show data source definitions, directed acyclic graphs (DAG),and a plotting object in accordance with some implementations.

FIGS. 7A-7C show plotting code including data source definitions, adirected acyclic graph (DAG), and a plotting object in accordance withsome implementations.

FIGS. 8A-8C show plotting code and graphical user interfaces (GUIs) inaccordance with some implementations.

FIG. 9 is a flowchart of an example method of efficient distribution andsynchronization of processing and data in accordance with someimplementations.

FIGS. 10A-10C show code using multiple plot methods within the samequery to produce a chart with multiple series, code using a moreefficient optimized method to create the same chart as code with greaterefficiency.

DETAILED DESCRIPTION

Reference may be made herein to the Java programming language, Javaclasses, Java bytecode and the Java Virtual Machine (JVM) for purposesof illustrating example implementations. It will be appreciated thatimplementations can include other programming languages (e.g., groovy,Scala, R, Go, etc.), other programming language structures as analternative to or in addition to Java classes (e.g., other languageclasses, objects, data structures, program units, code portions, scriptportions, etc.), other types of bytecode, object code and/or executablecode, and/or other virtual machines or hardware implemented machinesconfigured to execute a data system query.

FIG. 1 is a diagram of an example computer data system and network 100showing an example data distribution configuration in accordance withsome implementations. In particular, the system 100 includes anapplication host 102, a periodic data import host 104, a query serverhost 106, a long-term file server 108, and a user data import host 110.While tables are used as an example data object in the descriptionbelow, it will be appreciated that the data system described herein canalso process other data objects such as mathematical objects (e.g., asingular value decomposition of values in a given range of one or morerows and columns of a table), TableMap objects, etc. A TableMap objectprovides the ability to lookup a Table by some key. This key representsa unique value (or unique tuple of values) from the columns aggregatedon in a byExternal( ) statement execution, for example. A TableMapobject is can be the result of a byExternal( ) statement executed aspart of a query. It will also be appreciated that the configurationsshown in FIGS. 1 and 2 are for illustration purposes and in a givenimplementation each data pool (or data store) may be directly attachedor may be managed by a file server.

The application host 102 can include one or more application processes112, one or more log files 114 (e.g., sequential, row-oriented logfiles), one or more data log tailers 116 and a multicast key-valuepublisher 118. The periodic data import host 104 can include a localtable data server, direct or remote connection to a periodic table datastore 122 (e.g., a column-oriented table data store) and a data importserver 120. The query server host 106 can include a multicast key-valuesubscriber 126, a performance table logger 128, local table data store130 and one or more remote query processors (132, 134) each accessingone or more respective tables (136, 138). The long-term file server 108can include a long-term data store 140. The user data import host 110can include a remote user table server 142 and a user table data store144. Row-oriented log files and column-oriented table data stores arediscussed herein for illustration purposes and are not intended to belimiting. It will be appreciated that log files and/or data stores maybe configured in other ways. In general, any data stores discussedherein could be configured in a manner suitable for a contemplatedimplementation.

In operation, the input data application process 112 can be configuredto receive input data from a source (e.g., a securities trading datasource), apply schema-specified, generated code to format the loggeddata as it's being prepared for output to the log file 114 and store thereceived data in the sequential, row-oriented log file 114 via anoptional data logging process. In some implementations, the data loggingprocess can include a daemon, or background process task, that isconfigured to log raw input data received from the application process112 to the sequential, row-oriented log files on disk and/or a sharedmemory queue (e.g., for sending data to the multicast publisher 118).Logging raw input data to log files can additionally serve to provide abackup copy of data that can be used in the event that downstreamprocessing of the input data is halted or interrupted or otherwisebecomes unreliable.

A data log tailer 116 can be configured to access the sequential,row-oriented log file(s) 114 to retrieve input data logged by the datalogging process. In some implementations, the data log tailer 116 can beconfigured to perform strict byte reading and transmission (e.g., to thedata import server 120). The data import server 120 can be configured tostore the input data into one or more corresponding data stores such asthe periodic table data store 122 in a column-oriented configuration.The periodic table data store 122 can be used to store data that isbeing received within a time period (e.g., a minute, an hour, a day,etc.) and which may be later processed and stored in a data store of thelong-term file server 108. For example, the periodic table data store122 can include a plurality of data servers configured to store periodicsecurities trading data according to one or more characteristics of thedata (e.g., a data value such as security symbol, the data source suchas a given trading exchange, etc.).

The data import server 120 can be configured to receive and store datainto the periodic table data store 122 in such a way as to provide aconsistent data presentation to other parts of the system.Providing/ensuring consistent data in this context can include, forexample, recording logged data to a disk or memory, ensuring rowspresented externally are available for consistent reading (e.g., to helpensure that if the system has part of a record, the system has all ofthe record without any errors), and preserving the order of records froma given data source. Consistent data can also include a view of the datathat is internally consistent for a given instant (e.g. a consistentdata snapshot at a given instant). If data is presented to clients, suchas a remote query processor (132, 134), then the data may be persistedin some fashion (e.g., written to disk).

The local table data server 124 can be configured to retrieve datastored in the periodic table data store 122 and provide the retrieveddata to one or more remote query processors (132, 134) via an optionalproxy (e.g., table data cache proxy (TDCP) 394 and/or 404 as shown inFIG. 3 and FIG. 4, respectively). Remote query processors (132, 134) canalso receive data from DIS 120 and/or LTDS 124 via the proxy.

The remote user table server (RUTS) 142 can include a centralizedconsistent data writer, as well as a data server that providesprocessors with consistent access to the data that it is responsible formanaging. For example, users can provide input to the system by writingtable data that is then consumed by query processors.

The remote query processors (132, 134) can use data from the data importserver 120, local table data server 124 and/or from the long-term fileserver 108 to perform queries. The remote query processors (132, 134)can also receive data from the multicast key-value subscriber 126, whichreceives data from the multicast key-value publisher 118 in theapplication host 102. The performance table logger 128 can logperformance information about each remote query processor and itsrespective queries into a local table data store 130. Further, theremote query processors can also read data from the RUTS, from localtable data written by the performance logger, or from user table dataread over NFS, for example.

It will be appreciated that the configuration shown in FIG. 1 is atypical example configuration that may be somewhat idealized forillustration purposes. An actual configuration may include one or moreof each server and/or host type. The hosts/servers shown in FIG. 1(e.g., 102-110, 120, 124 and 142) may each be separate or two or moreservers may be combined into one or more combined server systems. Datastores can include local/remote, shared/isolated and/or redundant. Anytable data may flow through optional proxies indicated by an asterisk oncertain connections to the remote query processors (e.g., table datacache proxy (TDCP) 392 or 404 as shown in FIG. 3B and FIG. 4,respectively). Also, it will be appreciated that the term “periodic” isbeing used for illustration purposes and can include, but is not limitedto, data that has been received within a given time period (e.g.,millisecond, second, minute, hour, day, week, month, year, etc.) andwhich has not yet been stored to a long-term data store (e.g., 140).

FIG. 2 is a diagram of an example computer data system 200 showing anexample administration/process control arrangement in accordance withsome implementations. The system 200 includes a production client host202, a controller host 204, a GUI host or workstation 206, and queryserver hosts 208 and 210. It will be appreciated that there may be oneor more of each of 202-210 in a given implementation.

The production client host 202 can include a batch query application 212(e.g., a query that is executed from a command line interface or thelike) and a real time query data consumer process 214 (e.g., anapplication that connects to and listens to tables created from theexecution of a separate query). The batch query application 212 and thereal time query data consumer 214 can connect to a remote querydispatcher 222 and one or more remote query processors (224, 226) withinthe query server host 1 208.

The controller host 204 can include a persistent query controller 216configured to connect to a remote query dispatcher 232 and one or moreremote query processors 228-230. In some implementations, the persistentquery controller 216 can serve as the “primary client” for persistentqueries and can request remote query processors from dispatchers, andsend instructions to start persistent queries. For example, a user cansubmit a query to 216, and 216 starts and runs the query every day. Inanother example, a securities trading strategy could be a persistentquery. The persistent query controller can start the trading strategyquery every morning before the market opened, for instance. It will beappreciated that 216 can work on times other than days. In someimplementations, the controller may require its own clients to requestthat queries be started, stopped, etc. This can be done manually, orscheduled (e.g., cron jobs). Some implementations can include “advancedscheduling” (e.g., auto-start/stop/restart, time-based repeat, etc.)within the controller.

The GUI/host workstation can include a user console 218 and a user queryapplication 220. The user console 218 can be configured to connect tothe persistent query controller 216. The user query application 220 canbe configured to connect to one or more remote query dispatchers (e.g.,232) and one or more remote query processors (228, 230).

FIG. 3 is a diagram of an example computing device 300 configured forefficient distribution and synchronization of plotting processing anddata in accordance with at least one implementation. The computingdevice 300 includes one or more processors 302, operating system 304,computer readable medium 306 and network interface 308. The memory 306can include connected application 310 and a data section 312 (e.g., forstoring caches, index data structures, column source maps, plottingobject 650, plotting object 750, etc.).

In operation, the processor 302 may execute the application 310 storedin the memory 306. The application 310 can include software instructionsthat, when executed by the processor, cause the processor to performoperations for efficient distribution and synchronization of plottingprocessing and data in accordance with the present disclosure (e.g.,performing one or more of 402-426, 502-526, and/or 902-922 describedbelow).

The application program 310 can operate in conjunction with the datasection 312 and the operating system 304.

FIG. 4 is a flowchart of an example method 400 of efficient distributionand synchronization of plotting processing and data in accordance withsome implementations. Processing begins at 402, where a client receivesa script. For example, the client can receive a script entered by a userthat includes plot commands such as, for example, a script like thatshown in FIG. 6A. Processing continues to 404.

At 404, the client transmits the script to a query processor. The clientand query processor can be running on the same or different hardware.For example, the client can transmit the script to a query processorremote from the client. In another example, the client and queryprocessor could be running on the same hardware. Processing continues to406.

At 406, the query processor received the script and determines that thescript includes a plot command to display a plot at the client.Processing continues to 408.

At 408, the query processor generates a preemptive table to store datato be used by the client to display the plot. In some embodiments, morethan one preemptive table can be used to display a plot as shown, forexample, in FIG. 6E. Processing continues to 410.

At 410, the query processor generates a plot object that includes plotinformation including an export table handle for the preemptive tableand/or plotting parameters. Processing continues to 412.

At 412, the query processor transmits the plot object to the client.Processing continues to 414 and/or 418.

At 414, the client receives the plot object and uses the exported tablehandle to create a local copy of the preemptive table, includingcreating a listener and subscribing to receive consistent updates to thepreemptive table from the query processor at the listener. In someembodiments, the client receiving the plot object can be different thanthe client at 402 and 404 (e.g., a first client can configure a plotthat a second client can retrieve) (e.g., a client can connect to apersistent query, receive a list of available plots already availablefor that persistent query, and receive a plot object for one or more ofthe plots already available for that the persistent query). Processingcontinues to 416.

At 416, the client generates a plot based on the local copy of thepreemptive table. In some embodiments, the client can generate an imageof the plot and store the image of the plot. In some such embodiments,the image can be stored for distribution via a network such as a publicnetwork (e.g., the Internet) or a private network (e.g., an intranet).

At 418, the query processor processes update(s) to the preemptive table.Processing continues to 420.

At 420, the query processor sends updates to the client. Processingcontinues to 422.

At 422, the client updates the local copy of the preemptive table.Processing continues to 424.

At 424, the client determines whether the plot generated at 416 shouldbe redrawn/updated. If so, processing continues 426.

At 426, the client redraws/updates the plot. In some embodiments theclient can create/redraw/update the plot using an appropriate frameworksuch as, for example, JFreeChart by Object Refinery Limited, OrsonCharts by Object Refinery Limited, and/or Highcharts by Highsoft.

It will be appreciated that, although not shown, the subscribing clientcan cancel their subscription to stop receiving updates from the queryprocessor, and that the TDCP may cancel its own data subscriptionsand/or discard data it no longer needs for any RQP. It will also beappreciated that, although not shown, the subscribing client can cancelor pause updates when a plot is not “in view” in a graphical userinterface (GUI) of the client (e.g., the plot is in a tab or window thatis not active and/or not in the foreground or some other GUI element ispreventing the plot from being displayed it the GUI) to reduce networktraffic and reduce client replotting/redrawing processing (and canresume/restart updates when the plot is again viewable in the GUI).

It will also be appreciated that 402-422 may be repeated in whole or inpart. For example, 418-420 may be repeated to provide the synchronizedclient with consistent updates to the preemptive table.

In some embodiments, a client can connect to an existing persistentquery and the persistent query can provide a list of plots, tables, andother widgets that can be displayed. In such embodiments, 402-406 do notneed to be performed and the client can select a widget from the listassociated with the persistent query and info on the selected widget canbe sent to the client, and the widget can be drawn.

FIG. 5 is a flowchart of an example method 500 of efficient distributionand synchronization of plotting processing and data in accordance withsome implementations. Processing begins at 502, where a remote queryprocessor (RQP) creates a table, table X, to store data associated witha plot to be displayed by a client. For example, table X can be createdas the result of an operation on table A, as shown in FIG. 6A.Processing continues to 504.

At 504, RQP generates an export table handle for table X andautomatically establishes a subscription for Client to receiveconsistent updates to table X from RQP. Processing continues to 510and/or 514.

At 510, Client receives the table handle and an initial data snapshotfrom RQP and stores the initial data snapshot in a table X′ (e.g., tableX′ in FIG. 6D) as its local copy of table X. In some embodiments, RQPcan create the data snapshot for transmission to Client using method1000 shown in FIG. 10 of U.S. patent application Ser. No. 15/813,127,entitled “COMPUTER DATA DISTRIBUTION ARCHITECTURE CONNECTING AN UPDATEPROPAGATION GRAPH THROUGH MULTIPLE REMOTE QUERY PROCESSORS” and filed onNov. 14, 2017 (hereinafter the '127 application), which is herebyincorporated by reference herein in its entirety as if fully set forthherein. Processing continues to 512.

At 512, Client creates a listener 2 to receive consistent updates totable X from RQP (e.g., although not shown, X′ in FIG. 6D can include alistener such as listener 2). Processing continues to 418.

At 514, worker 1 creates a listener 1 and adds listener 1 to the DAGdefining table X_export as a dependent of table X in the DAG structure(e.g., although not shown, X_export in FIG. 5C can include a listenersuch as listener 1). Processing continues to 516.

At 516, listener 1 receives an AMDR notification of an update to tableX, creates a changed data snapshot, and sends an AMDR notification andthe changed data snapshot to worker 2. Processing continues to 418.

At 518, RQP receives notification at listener 2 of an update to table X,the notification including an AMDR message and a changed data snapshotwhen data has changed. Processing continues to 520.

At 520, RQP applies the changes to table X′. Processing continues to522.

At 522, Client can propagate the AMDR changes to dependents of table X′to process changes through one or more DAGs of Client that include tableX′. In some embodiments, Client uses a locking mechanism when performing518, 520, 522, 524, and 526 to ensure that changes are applied to tableX′ and its dependents in a consistent manner, and to providesynchronization between such updates to table X′ and plotredraws/updates (e.g., GUI redraws), as shown for example, in FIG. 9 ofthe '127 application.

It will be appreciated that, although not shown, the subscribing Clientcan cancel their subscription to stop receiving updates from RQP, andthat the TDCP may cancel its own data subscriptions and/or discard datait no longer needs for any RQP. For example, Client can cancel itssubscription to table X when the associated plot is no longer beingdisplayed.

It will also be appreciated that 502-526 may be repeated in whole or inpart. For example, 516-524/526 may be repeated to continue providingClient with consistent updates to table X so that Client can continue toupdate/redraw the plot.

FIGS. 6A-F show data source definitions, directed acyclic graphs (DAG),and a plotting object in accordance with some implementations. In FIG.6A, example code 600 defines the data sources as tables (A, B, X, andY). From the first four lines of code 600 for the data sources, DAG 602can be generated as shown by the graph in FIG. 6B. DAG 602 in FIG. 6Bshows dependencies between the nodes, which correspond to table datasources.

Although DAG 602 in FIG. 6B includes only four nodes, DAGs can begenerated with more nodes in various configurations. For example, asshown in FIGS. 6A, 6B, 7, and 8 of the '127 application, also show datasource definitions and a corresponding directed acyclic graph (DAG) inaccordance with some implementations.

In FIG. 6A of the present disclosure, example code defines the datasources as tables (A, B, X, and Y), where A and B are a primary datasources. From the code for the data sources, a DAG can be generated asshown by the graph in FIG. 6B. DAG 602 in FIG. 6B shows dependenciesbetween the nodes, which correspond to table relationships defined inFIG. 6A.

Data sources can include market data (e.g., data received via multicastdistribution mechanism or through a tailer), system generated data,historical data, user input data from the remote user table server,tables programmatically generated in-memory, or something furtherupstream in the DAG. In general, anything represented in the data systemas an object (e.g., a table) and which can refresh itself/provide datacan be a data source. Also, data sources can include non-table datastructures which update, for example, mathematical data structures. Forexample, B=A.sumBy(“GroupCol”), where this creates a summationaggregation of table A as a new table B. The table B would then getupdated when A changes as described, for example, in the '127application. Similarly, minimum, maximum, variance, average, standarddeviation, first, last, by, etc. aggregations can be supported, such as,for example, t5=t4.stdBy(“GroupCol”), where this creates a standarddeviation aggregation of table t4 as a new table t5.

In some implementations, code can be converted into the in-memory datastructures holding the DAG. For example, the source code of FIG. 6A getsconverted into the DAG data structure in memory. The DAG connectivitycan change by executing code. For example, assume a set of code CODE1 isexecuted. CODE1 leads to a DAG1 being created. Data can be processedthrough DAG1, leading to table updates. Now assume that the user wantsto compute a few more tables. The user can run a few more lines of codeCODE2, which use variables computed in CODE1. The execution of CODE2leads to a change in the DAG. As a simple example, assume that the first3 lines in FIG. 6A are executed. The user could come along later andexecute line 4, which would modify the DAG data structure (i.e., addingY). Also, some implementations can permit other programs to listen tochanges from a node representing a data object (e.g., table or non-tableobject). For example, such programs could include a plotting backend andcould listen to changes from a node representing data used by theprogram and the plotting backend to generate a plot as well as updatethe plot as changes are processed through the DAG.

In some implementations, when a table changes, an applicationprogramming interface (API) can specify, for example, rows where add,modify, delete, or reindex (AMDR) changes were made. A reindex is achange in which a row is moved but the value contained in the row is notmodified. The API can also provide a mechanism to obtain a value priorto the most recent change. When the DAG is processed during the refresh,the AMDR info on “upstream” data objects (e.g., tables, etc.) or nodescan be used to compute changes in “downstream” data objects or nodes. Insome implementations, the entire DAG can be processed during the refreshcycle.

In general, a DAG can be comprised of a) dynamic nodes (DN); b) staticnodes (SN); and c) internal nodes (IN) that can include nodes with DNand/or SN and/or IN as inputs.

DNs are nodes of the graph that can change. For example, DN can be datasources that update as new data comes in. DN could also be timers thattrigger an event based on time intervals. In other examples, DN couldalso be MySQL monitors, specialized filtering criteria (e.g., update a“where” filter only when a certain event happens). Because these nodesare “sources”, they may occur as root nodes in the DAG. At the mostfundamental level, DN are root DAG nodes which change (e.g., are“alive”).

SNs are nodes of the DAG that do not change. For example, historicaldata does not change. IN are interior nodes of the DAG. The state of anIN can be defined by its inputs, which can be DN, SN, and or IN. If allof the IN inputs are “static”, the IN will be static. If one or more ofthe IN inputs is “dynamic”, the IN will be dynamic. IN can be tables orother data structures. For example, a “listener IN” can permit code tolisten to a node of the DAG. A listener node or associated listenermonitoring code can place (or “fire”) additional events (ornotifications) into a priority queue of a DAG.

In general, a DAG can be composed of static and/or dynamic subgraphs. Insome implementations, update processing occurs on dynamic subgraphs(because static subgraphs are not changing). In some suchimplementations, only dynamic nodes are in the DataMonitor loop. ForTables, change notification messages such as, for example, AMDR messagescan be used for communication within the DAG.

When query code is executed, the DAG is created or modified. As part ofthis process, the system records the order in which the DAG nodes wereconstructed in. This “construction ordering” can be used to determinethe order that nodes are processed in the DAG.

For example, consider:

-   -   a=db.i( . . . ), where a is a dynamic node (or DN)    -   b=a.where(“A=1”)    -   c=b.where(“B=2”)    -   d=c.join(b)

Assume (a) has changes to be processed during a refresh cycle. The orderof processing will be (a), (b), (c), and then (d).

When (d) is processed, it will process input changes from both (b) and(c) before creating AMDRs notification messages for (d). This orderingprevents (d) from creating more than one set of AMDRs per input change,and it can help ensure that all AMDRs are consistent with all data beingprocessed for the clock cycle. If this ordering were not in place, itmay be possible to get multiple ticks per cycle and some of the data canbe inconsistent. Also, the ordering can help ensure that joins produceconsistent results.

In some examples, a single data source is used more than once (i.e., hastwo or more child nodes in the DAG).

It will be appreciated that the implementations discussed above can useany update message format and are not limited to AMDR messages.

In some implementations, refresh processing of a DAG such as those shownin FIGS. 6B-6D can be performed generally as disclosed in the '127application.

FIGS. 6C-6E are diagrams illustrating how DAG 602 is modified as lines5-8 of code 600 in FIG. 6A are processed. Lines 5-8 of code 600 includeplot( ) commands to create a chart (or graph) (or plot) with threedifferent series (and stored as the variable “myPlot”). Each of lines5-8 adds a different series to the chart, the fifth line adding a firstseries based on columns “Col1” and “Col2” of table X, the sixth lineadding a second series based on columns “Col1” and “Col3” of table X,and the seventh line adding a third series based on columns “Col1” and“Col2” of table Y.

As discussed above, DAG 602 can be generated from the first four linesof code 600. When the fifth line of code 600 is processed, DAG 602 ismodified to include a preemptive table (X_export) that includes only thecolumns from table X that the fifth line of code specifies to be used bythe plot( ) command (columns “Col1” and “Col2”), as shown by DAG 604 inFIG. 6C. When the sixth line of code 600 is processed, DAG 604 ismodified such that preemptive table X_export includes only the columnsfrom table X that the fifth and sixth lines of code specified to be usedby the plot( ) command (columns “Col1”, “Col2”, “Col3”), as shown by DAG606 in FIG. 6D. When the seventh line of code 600 is processed, DAG 606is modified to include a preemptive table (Y_export) that includes onlythe columns from table Y that the seventh line of code specifies to beused by the plot( ) command (columns “Col1” and “Col2”), as shown by DAG608 in FIG. 6E. In some embodiments, the “_export” table handles are notadded to DAG 602 until after the “show( )” command in the last line isprocessed (this improves efficiency because the system can generate onlythe final preemptive table instead of all of the incremental preemptivetables needed to construct the final summary).

In some embodiments, export table handles such as X_export support thefull suite of table operations, but execute everything exceptsubscription requests via operating on the table being exported (e.g.,table X) to create a new result table Z (not shown), and then on table Zto create a new subscription table Z_export (not shown). X_exportadditionally maintains state to keep track of pending index changes andsnapshot delivery for all subscribed/subscribing clients (queryprocessors and/or end user clients), batched up where subscriptionoverlap permits, as shown by X_export in FIG. 6D where the columns oftable X specified by the fifth and sixth lines of code 600 are batchedtogether in X_export.

FIG. 6E is a diagram illustrating a DAG 608 connected through a RQP 680and a Client 682, in accordance with some implementations. RQP 680comprises DAG 608 and Client 682 comprises local copies or replicas ofthe X_export and Y_export tables of RQP's DAG 608. Although not shown,client 682 can be coupled to one or more human input devices (e.g. adisplay device) to display a graphical user interface (GUI) and receiveinput from a user. In operation, Client transmits data to and receivesdata from RQP to efficiently distribute plotting processing andefficiently synchronize plotting data to receive consistent updates toplotting data generated by RQP in accordance with the methods shown, forexample, in FIGS. 4, 5, and 9, and described herein.

For example, after RQP 680 receives code 600 from client 682, exportedtable handles (with listeners) are added to the DAG as dependents oftables X and Y (shown as “X_export” and “Y_export” in FIG. 6E). The lastline of code 600 can cause RQP 680 to transmit a plotting data structuresuch as plotting object 650 shown in FIG. 6F to client 682. Plottingobject 650 includes a list 652 that contains export table handles 662,664, and 666 for the three plots 656, 658, and 660, respectively,defined by lines 5, 6, and 7 of code 600, respectively. For example,export table handle 662 provides a reference to columns “Col1” and“Col2” of X_export, export table handle 664 provides a reference tocolumns “Col1” and “Col3” of X_export, and export table handle 666provides a reference to columns “Col1” and “Col2” of Y_export. In someembodiments, client 682 can use the export table handles 662, 664, 666to transmit a subscription request to RQP 680 to receive consistentupdates as described in the '127 application. Alternatively, in someembodiments the subscription can be automatically applied by RQP 680without waiting for client 682 to submit such a subscription request, asdiscussed herein.

In some embodiments, a replica table such as table X′ is strictlyin-memory table—it keeps a full copy of the remote table X_export'sindex, and all snapshot data that it's currently subscribed to in sparsearray-backed column sources, with redirection indexes to allowcompaction and efficient changes.

In some embodiments, X′ and X′_2 of FIG. 6E are implemented as a singletable. In some embodiments, a single preemptive table with viewoperations can be used to create X′ and X′_2.

FIGS. 7A-7C show plotting code 700 including data source definitions, adirected acyclic graph (DAG), and a plotting object 750 in accordancewith some implementations. Code 700 is similar to code 600, with a pointsize plotting attribute added to each of the plots at lines 5-8 of code700. Plotting attributes such as point size can be added to plots andcan defined by reference to one or more columns of an object such as atable. For example, in FIG. 7A the point size of each plot is beingdefined by the “SizeCol” columns of tables X and Y. FIG. 7B shows thatcode 700 results in X_export and Y_export each including the “SizeCol”column. Plotting object 750 is similar to plotting object 650 shown inFIG. 6F, with plot attributes 754 added. Plot attributes 754 includes apoint size setting for each of the three plots defined by code 700. Forthe first plot (or “plot 0”), point size setting 768 is defined by tablehandle 744 which references the “SizeCol” column of X_export. For thesecond plot (or “plot 1”), point size setting 770 is set to table handle776 which references the “SizeCol” column of X_export. For the thirdplot (or “plot 1”), point size setting 772 is set to table handle 778which references the “SizeCol” column of Y_export.

Although not shown, additional plotting attributes can also be set suchas, for example: type of chart (e.g., bar charts, line charts, scatterplots, etc.), data point appearance (e.g., point shape, line color, linethickness, point size, etc), various text (e.g., axis labels, charttitles, tool tip displayed, axis ticks, etc.), chart appearance (e.g.,whether grid lines are displayed, what colors are used, etc.). Theplotting attributes can be set individually based on data in an object(e.g., table columns as shown in FIGS. 7A-C or arrays (e.g., “plot( . .. ).pointSize([1, 2, 3] as int[ ])”) to set the point size of individualpoints) or as a default using other values such as numeric values (e.g.,“plot( . . . ).pointSize(2)” to double the default size of a point). Inanother example, formatted tooltips can be specified for data points byproviding a formatting string (e.g., to show only a specific number ofdecimal places, to format a number as currency (e.g., adding a dollarsign), to format a number as a percent, to display a number inscientific notation, etc.), and the format string can be applied againsta specified object (e.g., table column or array). Plotting attributescan also be set by applying a function to data (e.g., data from thetable).

FIGS. 8A-8C show plotting code 800 and graphical user interfaces (GUIs)870 and 872 in accordance with some implementations. The first line ofcode 800 sources the data for six different “USyms” on Aug. 21, 2017.The second line of code 800 applies the “oneClick( )” method to thetable “t”; specifies the column “USym” should be enabled for OneClickfiltering; and saves the OneClick SelectableDataSet to the variable“toc”. The third line of the query creates a plot using data from thevariable “toc”; and saves the plot to the variable “RetailPlot”. Data inthe “Timestamp” column is used for the plot's X-axis values and datafrom the Last column is used for the plot's Y-axis values.

As shown in FIG. 8B, GUI 870 includes inactive tabs 840 and an activetab 802 that includes a single-input filter 804 set to filter on the“USym” column 808 and a graph area 806 displaying the plot saved to the“RetailPlot” variable. In operation, after an appropriate “USym” value810 is entered into single-input filter 804, a plot 812 for that “USym”value is displayed. After the “USym” value 850 is changed, a clientcomputer (not shown) executing GUIs 870/872 transmits the new value 850to the RQP processing the “RetailPlot” plot and then the RQP provides anew exported table handle to be used as the active table handle for the“RetailPlot”; and the client computer updates GUI 872 to display the newplot 852, in accordance with the methods disclosed herein such as, forexample method 900 of FIG. 9.

FIG. 9 is a flowchart of an example method 900 of efficient distributionand synchronization of processing and data in accordance with someimplementations. Processing begins at 902, where a Client creates aswitchable table object based on initial value(s) for each of one ormore changeable values (e.g., user input such as a one-click GUI input).In some embodiments the changeable values can be a user specified numberof random samples to downsample data (downsampling data with randomsamples allows for faster plotting on clients and can support clientswith limited memory and/or limited bandwith) (in such embodiments therandomly selected samples can be changed by getting a new export tablehandle to a new preemptive table). Processing continues to 904.

At 904, the client transmits initial value(s) to query processor.Processing continues to 906.

At 906, the query processor receives the initial value(s), creates apreemptive table based on the initial value(s), and transmits to theclient an export table handle to the preemptive table. Processingcontinues to 908.

At 908, the client receives the export table handle, creates a localcopy of the preemptive table, and subscribes to the query processor toreceive consistent updates to the preemptive table. Processing continuesto 910.

At 910, the client sets the local copy of the preemptive table to be theactive table of the switchable table object. Processing continues to912.

At 912, the client determines a value of the initial value(s) haschanged (e.g., a GUI input value has changed). Processing continues to914.

At 914, the client transmits updated value(s) to query processor.Processing continues to 916.

At 916, the query processor creates a new preemptive table based on theupdated value(s), and transmits to the client an export table handle tothe new preemptive table. Processing continues to 918.

It will be appreciated that 902-908 may be repeated in whole or in part,examples of which are shown as lines 920 and 922. For example, 908-918may be repeated to switch the active table of the switchable tableobject based on changes to the initial value(s) (e.g., switching out theactive table when a user modifies GUI input fields such as a one-clickGUI input).

FIGS. 10A-10C show code 1000 using multiple plot methods within the samequery to produce a chart with multiple series, code 1002 using a moreefficient optimized method to create the same chart 1012 as code 1000with greater efficiency, in accordance with some implementations.

In code 1000, four individual plot methods are required to generate theplot. Code 1002 uses the “plotBy( )” method to create the same chartcreated in code 1000, but with greater efficiency. In code 1002, onlyone table (“t6”) is generated by filtering the source table to containinformation about all four USyms. Then, the “plotBy( )” method uses“USym” (the last argument) as the grouping column, which enables theplot to show data for the four USyms in the table, as shown as 1004,1006, 1008, and 1010 of chart 1012 in FIG. 10C.

The “plotBy” group of methods can include “plotBy( )”, “catPlotBy( )”,and “ohlcPlotBy( )” and these methods can follow the same general syntaxas their respective plotting methods with an additional argument tospecify the grouping column to be used to plot multiple series. Thisgreatly simplifies and shortens the query structure, improvesefficiency, and enables plots that can adapt to the addition or removalof new groups. For example, if the second line of code 1002 wereomitted, the “ployBySample” plot can adapt to the addition or removal ofnew groups.

An XY Series Chart can be used to show values over a continuum, such astime. XY Series can be represented as a line, a bar, an area or as acollection of points. The X axis can be used to show the domain, whilethe Y axis can show the related values at specific points in the range.The syntax for this method can be: plot(“SeriesName”, source, “xCol”,“yCol”), where “SeriesName” is the name (as a string) you want to use toidentify the series on the chart itself, source is the table that holdsthe data you want to plot, “xCol” is the name of the column of data tobe used for the X value, “yCol” is the name of the column of data to beused for the Y value. The “plotBy” version of this method can have thefollowing syntax: plot(“SeriesName”, source, “xCol”, “yCol”,“groupByCol”), where “groupByCol” enables users to specify the groupingcolumn(s) to be used to plot multiple series (there can be more than onegrouping column in which case an additional argument is added for eachadditional grouping column (e.g., “‘State’, ‘City’”).

Category charts display the values of data from different discretecategories. By default, values can be presented as vertical bars.However, the chart can be presented as a bar, a stacked bar, a line, anarea or a stacked area. The syntax for this method can be:catPlot(“SeriesName”, source, “CategoryCol”, “ValueCol”), where“SeriesName” is the name (string) you want to use to identify the serieson the chart itself, source is the table that holds the data you want toplot, “CategoryCol” is the name of the column (as a string) to be usedfor the categories, and “ValueCol” is the name of the column (as astring) to be used for the values. The “plotBy” version of this methodcan have the following syntax: catPlotBy(“SeriesName”, source,“CategoryCol”, “ValueCol”, “groupByCol”), where “groupByCol” enablesusers to specify the grouping column(s) to be used to plot multipleseries (there can be more than one grouping column in which case anadditional argument is added for each additional grouping column (e.g.,“‘State’, ‘City’”).

The Open, High, Low and Close (OHLC) chart typically shows four pricesof a security or commodity per time slice: the open and close of thetime slice, and the highest and lowest values reached during the timeslice. This charting method can use a dataset that includes one columncontaining the values for the X axis (time), and one column for each ofthe corresponding four values (open, high, low, close) and has thefollowing syntax: ohlcPlot(“SeriesName”, source, “Time”, “Open”, “High”,“Low”, “Close”), where “SeriesName” is the name (as a string) you wantto use to identify the series on the chart itself, source is the tablethat holds the data you want to plot, “Time” is the name (as a string)of the column to be used for the X axis, “Open” is the name of thecolumn (as a string) holding the opening price, “High” is the name ofthe column (as a string) holding the highest price, and “Low” is thename of the column (as a string) holding the lowest price, “Close” isthe name of the column (as a string) holding the closing price. The“plotBy” version of this method can have the following syntax:ohlcPlotBy(“SeriesName”, source, “Time”, “Open”, “High”, “Low”, “Close”,“groupByCol”), where “groupByCol” enables users to specify the groupingcolumn(s) to be used to plot multiple series (there can be more than onegrouping column in which case an additional argument is added for eachadditional grouping column (e.g., “‘State’, ‘City’”).

Although references have been made herein to tables and table data, itwill be appreciated that the disclosed systems and methods can beapplied with various computer data objects to, for example, provideflexible data routing and caching for such objects in accordance withthe disclosed subject matter. For example, references herein to tablescan include a collection of objects generally, and tables can includecolumn types that are not limited to scalar values and can includecomplex types (e.g., objects).

It will be appreciated that the modules, processes, systems, andsections described above can be implemented in hardware, hardwareprogrammed by software, software instructions stored on a nontransitorycomputer readable medium or a combination of the above. A system asdescribed above, for example, can include a processor configured toexecute a sequence of programmed instructions stored on a nontransitorycomputer readable medium. For example, the processor can include, butnot be limited to, a personal computer or workstation or other suchcomputing system that includes a processor, microprocessor,microcontroller device, or is comprised of control logic includingintegrated circuits such as, for example, an Application SpecificIntegrated Circuit (ASIC), a field programmable gate array (FPGA), agraphics processing unit (e.g., GPGPU or GPU) or the like. Theinstructions can be compiled from source code instructions provided inaccordance with a programming language such as Java, C, C++, C # .net,assembly or the like. The instructions can also comprise code and dataobjects provided in accordance with, for example, the Visual Basic™language, a specialized database query language, or another structuredor object-oriented programming language. The sequence of programmedinstructions, or programmable logic device configuration software, anddata associated therewith can be stored in a nontransitorycomputer-readable medium such as a computer memory or storage devicewhich may be any suitable memory apparatus, such as, but not limited toROM, PROM, EEPROM, RAM, flash memory, disk drive and the like.

Furthermore, the modules, processes systems, and sections can beimplemented as a single processor or as a distributed processor.Further, it should be appreciated that the steps mentioned above may beperformed on a single or distributed processor (single and/ormulti-core, or cloud computing system). Also, the processes, systemcomponents, modules, and sub-modules described in the various figures ofand for embodiments above may be distributed across multiple computersor systems or may be co-located in a single processor or system. Examplestructural embodiment alternatives suitable for implementing themodules, sections, systems, means, or processes described herein areprovided below.

The modules, processors or systems described above can be implemented asa programmed general purpose computer, an electronic device programmedwith microcode, a hard-wired analog logic circuit, software stored on acomputer-readable medium or signal, an optical computing device, anetworked system of electronic and/or optical devices, a special purposecomputing device, an integrated circuit device, a semiconductor chip,and/or a software module or object stored on a computer-readable mediumor signal, for example.

Embodiments of the method and system (or their sub-components ormodules), may be implemented on a general-purpose computer, aspecial-purpose computer, a programmed microprocessor or microcontrollerand peripheral integrated circuit element, an ASIC or other integratedcircuit, a digital signal processor, a hardwired electronic or logiccircuit such as a discrete element circuit, a programmed logic circuitsuch as a PLD, PLA, FPGA, PAL, GP, GPU, or the like. In general, anyprocessor capable of implementing the functions or steps describedherein can be used to implement embodiments of the method, system, or acomputer program product (software program stored on a nontransitorycomputer readable medium).

Furthermore, embodiments of the disclosed method, system, and computerprogram product (or software instructions stored on a nontransitorycomputer readable medium) may be readily implemented, fully orpartially, in software using, for example, object or object-orientedsoftware development environments that provide portable source code thatcan be used on a variety of computer platforms. Alternatively,embodiments of the disclosed method, system, and computer programproduct can be implemented partially or fully in hardware using, forexample, standard logic circuits or a VLSI design. Other hardware orsoftware can be used to implement embodiments depending on the speedand/or efficiency requirements of the systems, the particular function,and/or particular software or hardware system, microprocessor, ormicrocomputer being utilized. Embodiments of the method, system, andcomputer program product can be implemented in hardware and/or softwareusing any known or later developed systems or structures, devices and/orsoftware by those of ordinary skill in the applicable art from thefunction description provided herein and with a general basic knowledgeof the software engineering and computer networking arts.

Moreover, embodiments of the disclosed method, system, and computerreadable media (or computer program product) can be implemented insoftware executed on a programmed general purpose computer, a specialpurpose computer, a microprocessor, or the like.

It is, therefore, apparent that there is provided, in accordance withthe various embodiments disclosed herein, methods, systems and computerreadable media for computer data distribution architecture connecting anupdate propagation graph through multiple remote query processors.

Application Ser. No. 15/813,127, entitled “COMPUTER DATA DISTRIBUTIONARCHITECTURE CONNECTING AN UPDATE PROPAGATION GRAPH THROUGH MULTIPLEREMOTE QUERY PROCESSORS” and filed in the United States Patent andTrademark Office on Nov. 14, 2017, is hereby incorporated by referenceherein in its entirety as if fully set forth herein.

Application Ser. No. 15/813,112, entitled “COMPUTER DATA SYSTEM DATASOURCE REFRESHING USING AN UPDATE PROPAGATION GRAPH HAVING A MERGED JOINLISTENER” and filed in the United States Patent and Trademark Office onNov. 14, 2017, is hereby incorporated by reference herein in itsentirety as if fully set forth herein.

Application Ser. No. 15/813,142, entitled “COMPUTER DATA SYSTEM DATASOURCE HAVING AN UPDATE PROPAGATION GRAPH WITH FEEDBACK CYCLICALITY” andfiled in the United States Patent and Trademark Office on Nov. 14, 2017,is hereby incorporated by reference herein in its entirety as if fullyset forth herein.

Application Ser. No. 15/813,119, entitled “KEYED ROW SELECTION” andfiled in the United States Patent and Trademark Office on Nov. 14, 2017,is hereby incorporated by reference herein in its entirety as if fullyset forth herein.

While the disclosed subject matter has been described in conjunctionwith a number of embodiments, it is evident that many alternatives,modifications and variations would be, or are, apparent to those ofordinary skill in the applicable arts. Accordingly, Applicants intend toembrace all such alternatives, modifications, equivalents and variationsthat are within the spirit and scope of the disclosed subject matter.

What is claimed is:
 1. A computer database system comprising: one ormore processors; computer readable storage coupled to the one or moreprocessors, the computer readable storage having stored thereoninstructions that, when executed by the one or more processors, causethe one or more processors to perform operations including: receiving,at a remote query processor, a plot command to generate a plot at aclient computer, the plot command referencing a first object, the firstobject being updatable by propagating updates through an updatepropagation graph associated with the first object; generating, at theremote query processor, a plotting data structure comprising an exportobject handle referencing at least a portion of the first object;transmitting, at the remote query processor, one or more messages to theclient computer, the one or more messages comprising the plotting datastructure and an initial snapshot of the first object; automaticallysubscribing, at the remote query processor, the client computer toreceive consistent updates to the first object; receiving, at the clientcomputer, the one or more messages comprising the plotting datastructure and the initial snapshot from the remote query processor;creating, at the client computer, a second object to represent a replicaof the portion of the first object referenced by the export objecthandle; storing, at the client computer, the initial snapshot as thereplica of the portion of the first object referenced by the exportobject handle; assigning, at the client computer, the replica as aninput to a figure to be displayed in a graphical user interface;generating, at the client computer, a graphical figure comprising theplot based on the plotting data structure and the replica of the portionof the first object referenced by the export object handle; adding atthe remote query processor a first listener to the update propagationgraph as a dependent of the first object; receiving, at the firstlistener, an update notification indicating an update to the firstobject; sending, by the remote query processor, a notification to theclient computer including an indication of the change to the firstobject and a copy of any changed data; responsive to receiving thenotification at the client computer, updating the replica of the portionof the first object referenced by the export object handle; andupdating, at the client computer, the graphical figure comprising theplot based on the plotting data structure and the updated replica of theportion of the first object referenced by the export object handle. 2.The system of claim 1, wherein the plotting data structure comprises theinitial snapshot.
 3. The system of claim 1, the operations furtherincluding: receiving, at the client computer, a request for thegraphical figure from a remote computer; and transmitting, at the clientcomputer, the graphical figure in an image format to the remotecomputer.
 4. The system of claim 3, wherein the image format is selectedfrom a group consisting of JPEG, GIF, PNG, SVG, and PDF.
 5. The systemof claim 1, wherein the updating the graphical figure is performed afterat least a portion of the graphical figure is visible in the graphicaluser interface.
 6. The system of claim 1, wherein the updating thegraphical figure is throttled such that the updating is performed aspart of a batch update.
 7. The system of claim 1, wherein the plottingdata structure comprises a second export object handle referencing asecond object to define an attribute of the plot.
 8. A methodcomprising: receiving, at a remote query processor, a plot command togenerate a plot at a client computer, the plot command referencing afirst object, the first object being updatable by propagating updatesthrough an update propagation graph associated with the first object;generating, at the remote query processor, a plotting data structurecomprising an export object handle referencing at least a portion of thefirst object; transmitting, at the remote query processor, one or moremessages to the client computer, the one or more messages comprising theplotting data structure and an initial snapshot of the first object;receiving, at the client computer, the one or more messages comprisingthe plotting data structure and the initial snapshot from the remotequery processor; creating, at the client computer, a second object torepresent a replica of the portion of the first object referenced by theexport object handle; storing the initial snapshot as the replica of theportion of the first object referenced by the export object handle;assigning, at the client computer, the replica as an input to a figureto be displayed in a graphical user interface; generating, at the clientcomputer, a graphical figure comprising the plot based on the plottingdata structure and the replica of the portion of the first objectreferenced by the export object handle; adding at the remote queryprocessor a first listener to the update propagation graph as adependent of the first object; receiving, at the first listener, anupdate notification indicating an update to the first object; sending,by the remote query processor, a notification to the client computerincluding an indication of the change to the first object and a copy ofany changed data; responsive to receiving the notification at the clientcomputer, updating the replica of the portion of the first objectreferenced by the export object handle; and updating, at the clientcomputer, the graphical figure comprising the plot based on the plottingdata structure and the updated replica of the portion of the firstobject referenced by the export object handle.
 9. The method of claim 8,wherein the plotting data structure comprises the initial snapshot. 10.The method of claim 8, the operations further including: receiving, atthe client computer, a request for the graphical figure from a remotecomputer; and transmitting, at the client computer, the graphical figurein an image format to the remote computer in response to the requestfrom the remote computer.
 11. The method of claim 10, wherein the imageformat is selected from a group consisting of JPEG, GIF, PNG, SVG, andPDF.
 12. The method of claim 8, wherein the updating the graphicalfigure is performed after at least a portion of the graphical figure isvisible in the graphical user interface.
 13. The method of claim 8,wherein the updating the graphical figure is throttled such that theupdating is performed as part of a batch update.
 14. The method of claim8, wherein the plotting data structure comprises a second export objecthandle referencing a second object to define an attribute of the plot.15. A nontransitory computer readable medium having stored thereonsoftware instructions that, when executed by one or more processors,cause the one or more processors to perform operations including:receiving, at a remote query processor, a plot command to generate aplot at a client computer, the plot command referencing a first object,the first object being updatable by propagating updates through anupdate propagation graph associated with the first object; generating,at the remote query processor, a plotting data structure comprising anexport object handle referencing at least a portion of the first object;transmitting, at the remote query processor, one or more messages to theclient computer, the one or more messages comprising the plotting datastructure and an initial snapshot of the first object; receiving, at theclient computer, the one or more messages comprising the plotting datastructure and the initial snapshot from the remote query processor;creating, at the client computer, a second object to represent a replicaof the portion of the first object referenced by the export objecthandle; storing the initial snapshot as the replica of the portion ofthe first object referenced by the export object handle; assigning, atthe client computer, the replica as an input to a figure to be displayedin a graphical user interface; generating, at the client computer, agraphical figure comprising the plot based on the plotting datastructure and the replica of the portion of the first object referencedby the export object handle; adding at the remote query processor afirst listener to the update propagation graph as a dependent of thefirst object; receiving, at the first listener, an update notificationindicating an update to the first object; sending, by the remote queryprocessor, a notification to the client computer including an indicationof the change to the first object and a copy of any changed data;responsive to receiving the notification at the client computer,updating the replica of the portion of the first object referenced bythe export object handle; and updating, at the client computer, thegraphical figure comprising the plot based on the plotting datastructure and the updated replica of the portion of the first objectreferenced by the export object handle.
 16. The nontransitory computerreadable medium of claim 15, wherein the plotting data structurecomprises the initial snapshot.
 17. The nontransitory computer readablemedium of claim 15, the operations further including: receiving, at theclient computer, a request for the graphical figure from a remotecomputer; and transmitting, at the client computer, the graphical figurein an image format to the remote computer in response to the requestfrom the remote computer.
 18. The nontransitory computer readable mediumof claim 15, wherein the updating the graphical figure is performed onlywhen at least a portion of the graphical figure is visible in thegraphical user interface.
 19. The nontransitory computer readable mediumof claim 15, wherein the updating the graphical figure is throttled suchthat the updating is performed as part of a batch update.
 20. Thenontransitory computer readable medium of claim 15, wherein the plottingdata structure comprises a second export object handle referencing asecond object to define an attribute of the plot.
 21. The nontransitorycomputer readable medium of claim 15, the operations further comprisingautomatically subscribing, at the remote query processor, the clientcomputer to receive consistent updates to the first object.
 22. Themethod of claim 8, the operations further comprising automaticallysubscribing, at the remote query processor, the client computer toreceive consistent updates to the first object.
 23. The system of claim1, wherein the first object is a table and the export object handle isan export table handle.
 24. The method of claim 8, wherein the firstobject is a table and the export object handle is an export tablehandle.
 25. The nontransitory computer readable medium of claim 15,wherein the first object is a table and the export object handle is anexport table handle.
 26. The system of claim 1, wherein the operationsfurther include: determining that the graphical figure is not beingdisplayed by the client computer; and responsive to the determining thatthe graphical figure is not being displayed, setting a mode of the plotto a sleep mode.
 27. The system of claim 26, wherein the sleep modeignores or prevents redraw events for the plot.
 28. The method of claim8, further comprising: determining that the graphical figure is notbeing displayed by the client computer; and responsive to thedetermining that the graphical figure is not being displayed, setting amode of the plot to a sleep mode.
 29. The method of claim 28, whereinthe sleep mode stops updates to the first object from being received.30. The nontransitory computer readable medium of claim 15, theoperations further comprising: determining that the graphical figure isnot being displayed by the client computer; and responsive to thedetermining that the graphical figure is not being displayed, setting amode of the plot to a sleep mode.